NOWNUNM: Nonlocal Weighted Nuclear Norm Minimization for Sparse-Sampling CT Reconstruction
Computed tomography (CT) image reconstruction using classical total variation (TV)-based methods or its variations inevitably suffers from a blocky effect when the sampling number is low, because of the piecewise assumption. A low-rank based method is an effective way to circumvent this side effect....
Main Authors: | Yi Zhang, Kang Yang, Yining Zhu, Wenjun Xia, Peng Bao, Jiliu Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2018-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8543791/ |
Similar Items
-
Helical CT Reconstruction From Sparse-View Data Through Exploiting the 3D Anatomical Structure Sparsity
by: Yongbo Wang, et al.
Published: (2021-01-01) -
Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT image Reconstruction
by: Yongchae Kim, et al.
Published: (2020-06-01) -
Sparse View CT Image Reconstruction Based on Total Variation and Wavelet Frame Regularization
by: Zhaoyan Qu, et al.
Published: (2020-01-01) -
Hybrid-Weighted Total Variation and Nonlocal Low-Rank-Based Image Compressed Sensing Reconstruction
by: Hui Zhao, et al.
Published: (2020-01-01) -
Adaptive Weighted Total Variation Minimization Based Alternating Direction Method of Multipliers for Limited Angle CT Reconstruction
by: Fulin Luo, et al.
Published: (2018-01-01)